Personal top 10 fallacies and paradoxes in statistics 1. Absence of evidence fallacy 2. Ecological fallacy 3. Stein’s paradox 4. Lord’s paradox 5. Simpson’s paradox 6. Berkson’s paradox 7. Prosecutors fallacy 8. Gambler’s fallacy 9. Lindsey’s paradox 10. Low birthweight paradox
1. Absence of evidence fallacy
Absence of evidence is not the same as evidence of absence. Wouldn't it be great if not statistically significant would just mean "no effect"? bmj.com/content/311/70…
2. Ecological fallacy
Hard to resist those sweet population level data to make inferences about health effects on the individual level web.stanford.edu/class/ed260/fr…
3. Stein's paradox
If your goal is prediction, you may *not* be after unbiased predictor effects in your prediction model
Perhaps one of the most famous paradoxes in statistics. Reversal of the direction of effect by simply combining two groups is something that may keep awake at night academic.oup.com/ije/article/40…
6. Berkson’s paradox
Also known as collider bias, something we have seen plenty in the COVID-19 literature
Pr(B|A) is not Pr(A|B). Confusing sensitivity/specificity for predictive values, p-values for probabilities about the hypothesis,.... the prosecutor's fallacy list is long
Arguably the odd one in the list, but cognitive biases about probabilities of recurrent events are very real and relevant en.wikipedia.org/wiki/Gambler%2…
9. Lindley’s paradox (not Lindsey's...)
If you are interested in the Bayesian vs frequentist statistics wars, make sure you study the paradox
How do I know how to become a successful academic? I don't, but I have received plenty of advice. As a good academic, I will just summarize what I have learned from listening
1) Be the ultimate collaborator but also don't be
Say yes to as many collaborations as physically possible: co-produce papers, LEARN, co-write grants, DISCUSS, it is all about synergy. But also, collaborations slow you down, have your own ideas! Just say no to collaborations
Disclaimer: this top 10 is just personal opinion. I’m biased towards explanatory methods and statistics articles relevant to health research, particularly those relating to prediction
The order in which the articles appear is pseudo-random
1) The first one is related to the pandemic. Title and subtitle give away the conclusions, but the arguments are particularly well put
First, I send you emails to which you politely and quickly responded. Thanks. You seemed to agree with my critique, but you didn't show any initiative to change it or remove the model
@Laconic_doc@statsmethods@GSCollins Second, I am one of the authors of a reply to the OpenSAFELY study where we specifically mention their model falls short of developing a risk model. You seem to have ignored that and used their multivariable results anyway